GL-SeqNet: Global–Local Fusion for Intra-Cluster Tomato Harvesting Sequence Optimization

Tomato harvesting in protected horticulture is a critical task that faces challenges due to labor shortages, high environmental stress in greenhouses, and the complex nature of clustered fruit arrangements. This study proposes the global–local sequence ranking network (GL-SeqNet), a novel model designed to optimize intra-cluster tomato harvesting sequences by integrating global and local features using a deep learning approach. GL-SeqNet fuses the global structural information of tomato clusters with local fruit attributes to dynamically update the harvesting sequence, ensuring the optimal target is selected at each step. The model features a dual-stream architecture, comprising separate global and local backbones, and utilizes a ranking head for prioritizing intra-cluster targets. An AI-based image object-removal tool was used to simulate the dynamic structural changes within a cluster during harvesting, facilitating the creation of a state evolution dataset. The experimental results showed that the best overall performance was achieved with a global resolution of 112 × 112 and a local resolution of 56 × 56, yielding a Top-1 accuracy of 0.950, a position match rate (PMR) of 0.970, and an inference time of only 22.6 ms, along with faster convergence. The results underscore the potential of global and local fusion strategies and ranking-based learning for effective harvesting sequence optimization.